Readers will find more than 170 theoretical, algorithmic, and numerical exercises that deepen and enhance the reader's understanding of the topics. The author includes several subjects not typically found in optimization books-for example, optimality conditions in sparsity-constrained optimization, hidden convexity, and total least squares. The book also offers a large number of applications discussed theoretically and algorithmically, such as circle fitting, Chebyshev center, the Fermat-Weber problem, denoising, clustering, total least squares, and orthogonal regression and theoretical and algorithmic topics demonstrated by the MATLAB toolbox CVX and a package of m-files that is posted on the book's web site.
Audience: This book is intended for graduate or advanced undergraduate students of mathematics, computer science, and electrical engineering as well as other engineering departments. The book will also be of interest to researchers.
Contents: Chapter 1: Mathematical Preliminaries; Chapter 2: Optimality Conditions for Unconstrained Optimization; Chapter 3: Least Squares; Chapter 4: The Gradient Method; Chapter 5: Newton s Method; Chapter 6: Convex Sets; Chapter 7: Convex Functions; Chapter 8: Convex Optimization; Chapter 9: Optimization Over a Convex Set; Chapter 10: Optimality Conditions for Linearly Constrained Problems; Chapter 11: The KKT Conditions; Chapter 12: Duality
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